Nothing
summary.grm <-
function (object, ...) {
if (!inherits(object, "grm"))
stop("Use only with 'grm' objects.\n")
coefs <- if (object$IRT.param) IRT.parm(object, digits.abbrv = object$control$digits)$parms else object$coefficients
coef.tab <- if (!is.null(object$hessian)) {
p <- length(coefs)
coefs. <- coefs
ncatg <- sapply(coefs, length)
if (object$constrained)
coefs.[-p] <- lapply(coefs[-p], function (x) x[-length(x)])
coefs. <- unlist(coefs., use.names = FALSE)
if (all(!is.na(object$hessian) & is.finite(object$hessian))) {
ev <- eigen(object$hessian, TRUE, TRUE)$values
if (!all(ev >= -1e-06 * abs(ev[1])))
warning("Hessian matrix at convergence is not positive definite; unstable solution.\n")
} else
stop("Hessian matrix at convergence contains infinite or missing values; unstable solution.\n")
se <- if (object$IRT.param) IRT.parm(object, TRUE, digits.abbrv = object$control$digits)$se else sqrt(diag(vcov(object)))
z.vals <- coefs.
for (i in seq(along = z.vals))
z.vals[i] <- coefs.[i] / se[i]
d <- data.frame(value = coefs., std.err = se, z.vals = z.vals)
d <- if (object$constrained)
split(d[c(seq(1, nrow(d) - 1), rep(nrow(d), p)), ], c(rep(1:p, ncatg - 1), 1:p))
else
split(d, rep(1:p, ncatg))
names(d) <- names(coefs)
for (i in seq(along = coefs))
rownames(d[[i]]) <- names(coefs[[i]])
d
} else {
coefs <- lapply(coefs, function (x) cbind(value = x))
}
out <- list(coefficients = coef.tab)
out$logLik <- object$log.Lik
df <- sapply(object$coef, length)
df <- if (object$constrained) sum(df) - length(df) + 1 else sum(df)
out$AIC <- -2 * object$log.Lik + 2 * df
out$BIC <- -2 * object$log.Lik + log(nrow(object$X)) * df
out$max.sc <- object$max.sc
out$conv <- object$conv
out$counts <- object$counts
out$call <- object$call
out$control <- object$control
out$attr <- attr(object$X, "items")
out$ancr <- attr(object$X, "anchoring")
class(out) <- "summ.grm"
out
}
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